The signal energy detector is a basic component of several processing techniques which the Air Force through RADC has investigated and developed, that detect signals in the RF spectrum and also determine their signal-to-noise ratio (SNR), bandwidth (BW), and center frequency. This collection of algorithms/processing techniques, which were developed during the 70's and 80's, are called the Automatic Intercept Device (AID) and the Automatic Intercept Module (AIM), versions 1 and 2.
The energy detectors used in these techniques are typically designed to achieve a constant false-alarm rate (CFAR) performance. This is realized by assuming the probability density function of the noise present in a particular frequency band is known, and making a detection decision given the probability that this detection was caused by noise only. The detection threshold is set based upon the desired probability of false alarm, P.sub.F, probability of detection, P.sub.D, and the minimum required SNR.
The input to the detectors used in AID, AIM1 and AIM2 is an array of complex numbers representing the frequency content of a chosen frequency band of interest. This array is generated using a fast Fourier transform (FFT) with an appropriate data window (e.g., Hanning, Hamming, etc.). The time data is typically obtained from either a single channel of a channelized receiver, or the Intermediate Frequency (IF) output of a superheterodyne receiver. This IF output is bandlimited, downconverted to baseband, lowpass filtered to prevent aliasing, amplified and finally digitized by an analog-to-digital (A/D) converter. The AID and AIM1 detectors operate on the magnitude squared of the individual frequency bins of the FFT, known as the periodogram. In addition to using the periodogram or auto-power spectrum, the AIM2 technique also uses the cross-spectrum power for performing detection. To generate the cross-spectrum, a second parallel channel consisting of antenna and receiver through A/D converter is needed. This second channel must be matched in amplitude and phase response to the first channel with a known antenna spacing. Since the AIM2 technique is a dual channel system, it will not be described in further detail. The AID and AIM1 techniques are single channel techniques and as such will be elaborated upon.
There is shown in FIG. 1 is the generalized form of the AID detection scheme based on a N-point FFT, where the weighted average of K power spectral density estimates (periodograms) is taken. The purpose of the weighted average is to take into account noise level fluctuations from one N-point block of spectral data to the next. The weights are determined by calculating a noise level estimate for each periodogram and scaling or normalizing all of the frequency bins in proportion to this estimated noise level. This is done for each of the K periodograms prior to taking their average. The logarithm of this average is taken and another noise level estimate is made using this average. A threshold level is determined based on this noise level estimate and the desired P.sub.D and P.sub.F. Finally, each frequency bin of the averaged set of K periodograms is compared against this threshold in deciding whether there is signal activity (energy) in a particular bin. Typically the rank-select-threshold (RST) technique is used in estimating the noise level in the spectral estimate, although any single channel noise estimation technique can be used.
The main limitation of the AID signal energy detection technique arises from averaging K periodograms. Averaging is desirable since it reduces the variance (by an amount proportional to k/K) of the power spectral estimate obtained using the periodogram. The two main disadvantages are:
a) An undesirable time delay exists between the start of the FFT process and the output of the threshold comparison mainly caused by the averaging process. For example, if it takes 0.5 milliseconds (mS) to calculate a single N-point FFT and if 10 averages are taken, it will take a minimum of 10 .times.0.5=5.0 mS to generate a detection report.
b) Signals which turn on and off rapidly and remain off or switch to another frequency can potentially be averaged out by the AID detection scheme and as a result not be detected at all. As an example, if the A/D sample rate is 4.096E+6 Samples/sec and the FFT size, N, is 4096 points, then the amount of time per FFT dwell is 4096 / 4.096E+6=1.0 mS. For a signal which turns on for less than 1 mS and off for greater than K mS (K being the number of averages) the averaging will tend to suppress the signal energy, causing it to not be detected.
In FIGS. 2a, b and c there is shown the AIM 1 detection scheme as described in reference 2. Like AID, AIM1 is based on a N-point FFT used to form the periodogram as the spectral estimate. Although it is not discussed herein a weighted average could be incorporated into the technique.
The AIM1 technique uses the mode estimation technique for estimating the noise level in the spectral estimate. This noise estimate is used by the multiple threshold selection unit section to help establish the thresholds used by the detection processor unit. As with AID, any single channel noise estimation technique can be used. The noise estimate is used to establish a CFAR performance for each detector in the detection processor unit. Overall this results in a P.sub.F which is greater than that of the individual detectors. The operation of the AIM1 detection processor unit is shown in further detail in FIG. 2b. To improve the detection of wide bandwidth signals a bank of detectors is used which operates on multiple adjacent FFT bins. Each detector is the same in form but different with regard to how many frequency bins each detector averages, thus each detector has a different bandwidth (BW). The first detector, BW1, processes one bin at a time, the second detector, BW2, processes 3 bins at a time, the third detector, BW3, processes 5 bins, etc., until the widest bandwidth detector, BWN, makes a decision based on the whole spectrum which is made up of N frequency bins. In this case, the binary sequence (1,2,4,8, . . . ) was chosen for the sequence of detector widths although any reasonable number sequence could be used. The number sequence was forced to be odd, by adding 1 to the even numbers in the sequence, solely for the purpose of easing software proof-of-concept. As with averaging sets of consecutive spectral estimates, averaging multiple consecutive bins within a single estimate increases the time-bandwidth product of the detector and hence improves detector performance.
Once detections have been made, the results of the individual detectors must in some way be combined to form the resultant output of the AIM1 detection processor. For AIM1 the combination technique of FIG. 2c is used. This technique gives precedence to the detections which occurred in the narrower bandwidth detectors. Note that using this combination technique, it is computationally more efficient to postpone the frequency bin averaging in a particular detector (e.g. BW2) until detection has been completed in the more narrow bandwidth detectors (e.g. BW1). In this way, only the frequency bins not yet found to have signal energy need to be averaged. Finally, the detection results of each of the varying bandwidth detectors are summed (logically OR'd) to form the signal detections. Other combination techniques such as simply performing a logical OR directly on the detection outputs of each of the detectors, BW1 through BWN, could also be used.
Returning to FIG. 2a, the final stage of the AIM1 detector is the detection modifier unit. After signal energy detector is completed by the detection processor unit, the remaining portions of the spectrum should theoretically be noise. AIM1 checks for this condition in the mean-variance verifier unit using the mean-variance power detector technique. If the mean-variance detector decides that the supposed noise bands are not noise, then the detection modifier unit concludes that a noise floor must not have been present in the spectrum. In this case the entire spectrum is declared to contain signal. On the other hand, the mean-variance verifier unit may indicate that the original noise floor estimate was too high, possibly causing signal energy to be missed. In this case the detection thresholds can be lowered and, time permitting, the spectral data can be re-processed by the detection processor unit. As a minimum, an operator can be alerted to this desensitization caused by an incorrectly high noise floor estimate.
Although AIM1 may constitute a performance improvement over AID in terms of probability of false alarm, probability of detection and required SNR, AIM1 still suffers from the same limitations as AID. Namely:
a) An undesirable time delay between the start of the FFT process and the final output of the detection modifier unit, mainly caused by the averaging K blocks section.
b) Averaging out signal transitions of short time duration. In addition to these, AIM1 also suffers from:
c) A tendency to under estimate a signal's bandwidth.
This is caused by the AIM1 detection combiner unit which gives precedence to narrowband signals to prevent exaggerating the bandwidth. AIM1 therefore requires some form of bandwidth-expander to compensate for detecting small bandwidths.